With rapid advance of technology, there are more and more handheld electronic devices that are becoming available and commonly used in our daily lives, such as smart phones, tablet computers and notebook computers. Nevertheless, also because of their variety in design and style, many recent electronic devices can not be produced completely by an automation process, but still require plenty of manpower for assembly.
As a consequence, for increasing production and reducing cost, there are more and more studies in the industry trying to design a hybrid automation system capable of combining tasks that are needed to be accomplished accurately and rapidly and being executed by robots with tasks that are high complicated and needed to be performed by human into a same production line, and thereby, enjoying the benefic of both robotic assembly and manual assembly simultaneously.
However, in most workflows enabled in current hybrid automation systems there is no visual recognition apparatus being provided for monitoring the movement of both robots and human, whereas the movement of human operators are especially difficult to measure and quantified. In most cases, certain kinds of artificial intelligence will be needed just to identify the meaning of operator's hand movements, otherwise the workflow including alternating manual procedures and automated robotic procedures can not be performed smoothly. Therefore, it is in need of an improved workflow monitoring and analysis apparatus and method adapted for hybrid automation.